City, country, empire : landscapes in environmental history
Bibliographic record
Abstract
In the urgently expanding field of environmental history, two trends are emerging. Research has internationalized, crossing political and historical borders. And urban spaces are increasingly seen as part of, not apart from, the global environment. In this book, Jeffry Diefendorf and Kurk Dorsey have gathered much of the important work pushing the field in new directions. Eleven essays by prominent and regionally diverse scholars address how human and natural forces collaborate in the creation of cities, the countryside, and empires. The Cities section features essays that examine pollution and its aftermath in Pittsburgh, the Ruhr Valley (Germany), and Los Angeles. These urban areas are far apart on the globe but closely linked in their histories of how human decision making has affected the environment. Changing rural and suburban spaces are the focus of Countryside. Elizabeth Blackmar follows the money in order to understand why the financing of suburban mall developments makes local resistance difficult. Studies of the fractious history of the creation of a wildlife refuge in Oregon and the ongoing impact of hydraulic mining in the early California goldmining era emphasize the misuse of technology in rural spaces. Such misuse is a central idea of Empires. In When Stalin Learned to Fish, Paul R. Josephson tells the story of Soviet fishing technology designed to harness fish to the engine of socialism. Other essays explore the failures of Western agricultural technology in Africa and the relationship between such technology and disease in European attempts to conquer the Caribbean. In a stirring, wide-ranging consideration of the neo-European colonies (the United States, Australia, Canada, and New Zealand), Thomas R. Dunlap observes the ongoing, unsettled interaction of lands and dreams. An afterword by Alfred W. Crosby, an eminent scholar of environmental history, closes the book with a broad and insightful synthesis of the history and future of this critical field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".